| Course Title | Code | Semester | L+U Hour | Credits | ECTS |
|---|---|---|---|---|---|
| Data Mining | YBS214 | 4. Semester | 3 + 0 | 3.0 | 6.0 |
| Prerequisites | None |
| Language of Instruction | Turkish |
| Course Level | Undergraduate |
| Course Type | |
| Mode of delivery | FaceToFace |
| Course Coordinator |
Lect. Dr. Günay TEMÜR |
| Instructors |
Günay TEMÜR |
| Assistants | |
| Goals | Introducing data mining and providing information about its intended use. Gaining analysis skills on data sets. Introducing and using the programs to be used in data mining. |
| Course Content | -Explaining the basic concepts of Data Mining -Introducing the usage areas of Data Mining -Explaining the basic methods of Data Mining -Introducing the software used in Data Mining -Data analysis using various Data Mining methods |
| Learning Outcomes |
- Knows the definition and purpose of using data mining - Knows data mining processes - Gets information about the softwares used in data mining - Knows and applies data preprocessing processes - Knows basic data mining methods, applies them and interprets the results. |
| Week | Topics | Learning Methods |
|---|---|---|
| 1. Week | Introduction to Data Mining | Course Hours Research Preparation, After Class Study |
| 2. Week | Data Mining Basic Concepts, Background, Methods | Course Hours Research Preparation, After Class Study |
| 3. Week | Basic Python operations | Practice Preparation, After Class Study Course Hours Research |
| 4. Week | Basic Python Operations | Course Hours Preparation, After Class Study Research Practice |
| 5. Week | Basic Python Operations | Course Hours Research Preparation, After Class Study Practice |
| 6. Week | Dataset creation and preprocessing operations | Practice Preparation, After Class Study Research Course Hours |
| 7. Week | Regression Algorithms | Course Hours Research Practice Preparation, After Class Study |
| 8. Week | Classification Algorithms | Research Practice Preparation, After Class Study Course Hours |
| 9. Week | Classification Algorithms | |
| 10. Week | Classification Algorithms | Practice Course Hours Preparation, After Class Study |
| 11. Week | Association Rules | Practice Course Hours Research Preparation, After Class Study |
| 12. Week | Association Rules | Course Hours Practice Preparation, After Class Study |
| 13. Week | Clustering Algorithms | Research Preparation, After Class Study Practice Course Hours |
| 14. Week | Overview of The Term | Preparation, After Class Study Practice Course Hours |
| Course Slides |
| İlker Arslan, Data Science with Python, Pusula Press |
| Sadi Evren Seker- Youtube "Computer Concepts" Channel Data Mining Videos |
| Web pages related to the subject to be specified within the course |
| Program Requirements | Contribution Level | DK1 | DK2 | DK3 | DK4 | DK5 | Measurement Method |
|---|---|---|---|---|---|---|---|
| PY1 | 0 | 0 | 0 | 0 | 0 | 0 | - |
| PY2 | 5 | 5 | 5 | 0 | 5 | 5 | 40,60 |
| PY3 | 4 | 4 | 4 | 0 | 4 | 4 | 40,60 |
| PY4 | 5 | 5 | 5 | 0 | 5 | 5 | - |
| PY5 | 4 | 4 | 4 | 0 | 4 | 4 | - |
| PY6 | 3 | 3 | 3 | 0 | 3 | 3 | - |
| PY7 | 5 | 5 | 5 | 0 | 5 | 5 | 40,60 |
| PY8 | 5 | 5 | 5 | 0 | 5 | 5 | 40,60 |
| PY9 | 5 | 5 | 5 | 0 | 5 | 5 | - |
| PY10 | 3 | 3 | 3 | 0 | 3 | 3 | - |
| PY11 | 2 | 2 | 2 | 0 | 2 | 2 | - |
| PY12 | 1 | 1 | 1 | 0 | 1 | 1 | - |
| PY13 | 0 | 0 | 0 | 0 | 0 | 0 | - |
| PY14 | 3 | 3 | 3 | 0 | 3 | 3 | - |
| 0 | 1 | 2 | 3 | 4 | 5 | |
|---|---|---|---|---|---|---|
| Course's Level of contribution | None | Very Low | Low | Fair | High | Very High |
| Method of assessment/evaluation | Written exam | Oral Exams | Assignment/Project | Laboratory work | Presentation/Seminar |
| Event | Quantity | Duration (Hour) | Total Workload (Hour) |
|---|---|---|---|
| Course Hours | 14 | 3 | 42 |
| Research | 14 | 3 | 42 |
| Preparation, After Class Study | 14 | 3 | 42 |
| Practice | 10 | 2.5 | 25 |
| Midterm 1 | 1 | 1 | 1 |
| Final | 1 | 1 | 1 |
| Total Workload | 153 | ||
| ECTS Credit of the Course | 6.0 | ||
